source("002-LoadData.R")
library(stats)
library(forcats)
library(effsize)
library(clinfun)
library(ggplot2)
library(reshape2)
library(forcats)
#newcomer recurring, non recurring
print("Newcomers over time:")
## [1] "Newcomers over time:"
timelyAgg <- aggregate(newcomers$quantity[order(fct_rev(newcomers$timeRange))],
by=list(newcomers$Mapperid[order(fct_rev(newcomers$timeRange))],
newcomers$Happeningid[order(fct_rev(newcomers$timeRange))],
newcomers$HappeningType[order(fct_rev(newcomers$timeRange))]),
FUN=cumsum)
newcomersOverTime <- aggregate( timelyAgg$x>0, by=list(timelyAgg$Group.3) , FUN=sum)
newcomersOverTime$during <- summary(nonrecurringNewcomers$HappeningType)+newcomersOverTime$V4
1-newcomersOverTime[,-1]/newcomersOverTime[,6]
## V1 V2 V3 V4 during
## 1 0.8817204 0.8207885 0.7670251 0.7311828 0
## 2 0.9342105 0.8815789 0.7631579 0.5657895 0
## 3 0.9018692 0.8411215 0.7757009 0.6028037 0
newcomerPlot <- ggplot(melt(newcomersOverTime),aes(x=rev(variable),group=Group.1,color=Group.1,y=value))+
geom_line()+
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,size = 10, hjust = 1),
axis.text.y=element_text(size=10))+
scale_x_discrete(labels=c("during","one month","six months","one year","two years"))+
scale_colour_manual(values=c("#8c510a","#01665e","#5ab4ac"))+
labs(x = "Time interval of last contribution",y="Nr. of newcomers contributing",color="Happening type")
## Using Group.1 as id variables
print(newcomerPlot)
ggsave(path="/home/moritz/Schreibtisch/Masterarbeit/02_Text/figures",
filename = "newcomerPlot.eps",
plot=newcomerPlot,
units="cm",
width = 16,
height = 12)
print("for recurring newcomers only: ")
## [1] "for recurring newcomers only: "
digiArea <- c("element_density","tag_density","user_density","area_diversity")
contribIndepVar <- c("quantity","discussion_size","notes_size")
## statistics on newcomers
for (variableName in names(dependentVnewcomer.changes)){
print("")
print("========================================================================")
print(paste("RESULTS FOR VARIABLE",variableName))
print("========================================================================")
print("")
print("")
jitterplot <- ggplot(dependentVnewcomer.changes,aes(x=independentVnewcomer.mappers$timeRange,
y=dependentVnewcomer.changes[,variableName]))+
geom_count(position="jitter",aes(color=independentVnewcomer.mappers$HappeningType))+
labs(title=variableName)+
scale_color_manual(values=c("#8c510a","#01665e","#5ab4ac"))+
theme(legend.title = element_blank(),
axis.title.x = element_blank(),
axis.title.y=element_blank())+
scale_y_continuous(trans='pseudo_log')
print(jitterplot)
print("")
lineplot <- ggplot(dependentVnewcomer.changes,aes(x=independentVnewcomer.mappers$timeRange,
group=interaction(independentVnewcomer.mappers$Mapperid,independentVnewcomer.mappers$Happeningid),
y=dependentVnewcomer.changes[,variableName]))+
geom_line(aes(color=independentVnewcomer.mappers$HappeningType))+
labs(title=variableName)+
scale_color_manual(values=c("#8c510a","#01665e","#5ab4ac"))+
theme(legend.title = element_blank(),
axis.title.x = element_blank(),
axis.title.y=element_blank())+
scale_y_continuous(trans='pseudo_log')
print(lineplot)
print("")
print("Boxplot on contributors only: ")
boxplot <- ggplot(dependentVnewcomer.changes[dependentVnewcomer.changes$quantity>0,],
aes(x=independentVnewcomer.mappers[dependentVnewcomer.changes$quantity>0,"timeRange"],
color=independentVnewcomer.mappers[dependentVnewcomer.changes$quantity>0,"HappeningType"],
y=dependentVnewcomer.changes[dependentVnewcomer.changes$quantity>0,variableName]))+
geom_boxplot()+
labs(title=variableName)+
scale_color_manual(values=c("#8c510a","#01665e","#5ab4ac"))+
theme(legend.title = element_blank(),
axis.title.x = element_blank(),
axis.title.y=element_blank())+
scale_y_continuous(trans='pseudo_log')
print(boxplot)
print("")
for(i in levels(independentVnewcomer.mappers$timeRange)){
#skip forbidden variables
if(i=="one month"&any(digiArea==variableName)){
next
}
#get data for time interaval and variable
if(any(contribIndepVar==variableName)){
variable <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i,variableName]
classes <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i,"HappeningType"]
}else{
variable <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&dependentVnewcomer.changes$quantity>0,variableName]
classes <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&dependentVnewcomer.changes$quantity>0,"HappeningType"]
}
print("")
print(paste("Time frame: ",i))
print("")
print("N:")
print(summary(classes))
print("")
print(paste("Summary Statistics for variable ",variableName))
print(aggregate(variable,by=list(classes),FUN=summary))
print("")
#kruskal test
kruskal <- kruskal.test(x=variable,g=classes)
if(!is.nan(kruskal$p.value) & kruskal$p.value<=0.05){
print("")
print(paste(variableName,"is significantly influenced by an event for",i))
print("")
print("")
# wilcox pariwise post hoc
#http://www.sthda.com/english/wiki/kruskal-wallis-test-in-r
wilcox <- pairwise.wilcox.test(x=variable,g=classes,p.adjust.method = "BH")
print("Pairwise comparison: ")
print(wilcox$p.value)
# mode IVs for CFM
if(wilcox$p.value["CFM","CG"]<=0.05){
print(paste("Cohends d for effect size of the CFM on",variableName,":"))
print(cohen.d(d=variable[classes!="CRM"],f=fct_drop(classes[classes!="CRM"])))
print("")
if(any(contribIndepVar==variableName)){
variable1 <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&
independentVnewcomer.mappers$HappeningType=="CFM",variableName]
# economic status
classes1 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
independentVnewcomer.mappers$HappeningType=="CFM","economic_status"]
}else{
variable1 <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&
dependentVnewcomer.changes$quantity>0&
independentVnewcomer.mappers$HappeningType=="CFM",variableName]
# economic status
classes1 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
dependentVnewcomer.changes$quantity>0&
independentVnewcomer.mappers$HappeningType=="CFM","economic_status"]
}
jockey <- jonckheere.test(variable1,classes1,nperm=1000)
print("N:")
print(summary(classes1))
print("")
print("Summary Statistics: ")
print(aggregate(variable1,by=list(classes1),FUN=summary))
print("")
if(jockey$p.value<=0.05){
wilcox2 <- pairwise.wilcox.test(x=variable1,g=classes1,p.adjust.method = "BH")
if(any(wilcox2$p.value<=0.05,na.rm=TRUE)){
print("Pairwise comparison: ")
print(wilcox2$p.value)
print(paste("Analyses of the effect of the economic status for CFM with a p-value of",jockey$p.value))
{ sink("/dev/null"); print(plot(x=classes1,
y=variable1,
main="The economic status has an effect",
sub=paste("on this variable (",variableName,") for CFM")));
sink(); }
print(plot.new())
print("")
}
}
#culture
if(any(contribIndepVar==variableName)){
classes1 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
independentVnewcomer.mappers$HappeningType=="CFM","culture"]
}else{
classes1 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
dependentVnewcomer.changes$quantity>0&
independentVnewcomer.mappers$HappeningType=="CFM","culture"]
}
classes1 <- fct_drop(fct_lump_min(classes1,min=10))
print("N:")
print(summary(classes1))
print("")
print("Summary Statistics: ")
print(aggregate(variable1,by=list(classes1),FUN=summary))
print("")
if(length(levels(classes1))>1){
kruskal2 <- kruskal.test(x=variable1,g=classes1)
if(!is.nan(kruskal2$p.value) & kruskal2$p.value<=0.05){
wilcox2 <- pairwise.wilcox.test(x=variable1,g=classes1,p.adjust.method = "BH")
if(any(wilcox2$p.value<=0.05,na.rm=TRUE)){
print("")
print(paste(variableName,"is significantly influenced by at least one culture for CFM with p=",kruskal2$p.value))
print("")
print("Pairwise comparison: ")
print(wilcox2$p.value)
{ sink("/dev/null"); print(plot(classes1,variable1,main="The culture has an effect",
sub=paste("on this variable (",variableName,") for CFM"))); sink(); }
print(plot.new())
print("")
}
}
}
}
# other IVs for CRM
if(wilcox$p.value["CRM","CG"]<=0.05){
print(paste("Cohends d for effect size of the CRM on",variableName,":"))
print(cohen.d(d=variable[classes!="CFM"],f=fct_drop(classes[classes!="CFM"])))
print("")
if(any(contribIndepVar==variableName)){
variable2 <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&
independentVnewcomer.mappers$HappeningType=="CRM",variableName]
## distance during event
classes2 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
independentVnewcomer.mappers$HappeningType=="CRM","event_mapping_distance"]
}else{
variable2 <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&
dependentVnewcomer.changes$quantity>0&
independentVnewcomer.mappers$HappeningType=="CRM",variableName]
## distance during event
classes2 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
dependentVnewcomer.changes$quantity>0&
independentVnewcomer.mappers$HappeningType=="CRM","event_mapping_distance"]
}
print("N:")
print(length(classes2))
print("")
model <- lm(variable2~classes2)
if(any(summary(model)$coefficients[2,4]<=0.05,na.rm = TRUE)){
print("Analyses of the effect of the distance to the Region mapped during the event for CRM")
{ sink("/dev/null"); print(plot(classes2,variable2,log="x",main="The event mapping distance has an effect",
sub=paste("on this variable (",variableName,") for CRM"))); sink(); }
print(abline(model))
print(plot.new())
print(summary(model))
print("")
}
}
# between happenings
if(!is.nan(wilcox$p.value["CRM","CFM"]) & wilcox$p.value["CRM","CFM"]<=0.05){
print(paste("Cohends d for effect size between CRM and CFM on",variableName,":"))
print(cohen.d(d=variable[classes!="CG"],f=fct_drop(classes[classes!="CG"])))
print("")
}
}
print("")
print("")
print("--------------------------------------------------------------------------------")
print("")
print("")
}
}
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE quantity"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable quantity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000 0.00000 0.00000 106.88000 1.00000
## 2 CFM 0.00000 1.00000 10.00000 87.60606 66.00000
## 3 CRM 0.00000 0.00000 14.00000 102.36471 84.00000
## x.Max.
## 1 5621.00000
## 2 1628.00000
## 3 2021.00000
## [1] ""
## [1] ""
## [1] "quantity is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
## CG CFM
## CFM 6.700544e-06 NA
## CRM 7.499466e-07 0.9007524
## [1] "Cohends d for effect size of the CFM on quantity :"
##
## Cohen's d
##
## d estimate: 0.03301285 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.3811621 0.4471879
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 2 4 13
## high income
## 14
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 low income 10.0000 10.0000 10.0000 10.0000 10.0000
## 2 lower middle income 0.0000 0.0000 0.0000 34.7500 34.7500
## 3 upper middle income 0.0000 3.0000 34.0000 167.6154 104.0000
## 4 high income 0.0000 0.2500 3.0000 39.5000 43.7500
## x.Max.
## 1 10.0000
## 2 139.0000
## 3 1628.0000
## 4 323.0000
## [1] ""
## [1] "N:"
## Latin American Other
## 20 13
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 Latin American 0.0000 1.5000 12.5000 34.7500 68.7500
## 2 Other 0.0000 1.0000 10.0000 168.9231 51.0000
## x.Max.
## 1 111.0000
## 2 1628.0000
## [1] ""
## [1] "Cohends d for effect size of the CRM on quantity :"
##
## Cohen's d
##
## d estimate: 0.009065739 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.3038371 0.3219686
## [1] ""
## [1] "N:"
## [1] 85
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable quantity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000 0.00000 0.00000 184.38667 4.50000
## 2 CFM 0.00000 0.00000 0.00000 51.72727 32.00000
## 3 CRM 0.00000 0.00000 0.00000 59.67059 23.00000
## x.Max.
## 1 5427.00000
## 2 604.00000
## 3 1459.00000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable quantity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.000000 0.000000 0.000000 309.920000 6.500000
## 2 CFM 0.000000 0.000000 0.000000 8.575758 0.000000
## 3 CRM 0.000000 0.000000 0.000000 44.929412 0.000000
## x.Max.
## 1 21582.000000
## 2 208.000000
## 3 1847.000000
## [1] ""
## [1] ""
## [1] "quantity is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.02043907 NA
## CRM 0.12334534 0.2015395
## [1] "Cohends d for effect size of the CFM on quantity :"
##
## Cohen's d
##
## d estimate: 0.144717 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.2698939 0.5593280
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 2 4 13
## high income
## 14
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean
## 1 low income 0.000000 0.000000 0.000000 0.000000
## 2 lower middle income 0.000000 0.000000 0.000000 0.000000
## 3 upper middle income 0.000000 0.000000 0.000000 16.000000
## 4 high income 0.000000 0.000000 0.000000 5.357143
## x.3rd Qu. x.Max.
## 1 0.000000 0.000000
## 2 0.000000 0.000000
## 3 0.000000 208.000000
## 4 1.500000 63.000000
## [1] ""
## [1] "N:"
## Latin American Other
## 20 13
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 Latin American 0.00000 0.00000 0.00000 0.15000 0.00000
## 2 Other 0.00000 0.00000 0.00000 21.53846 2.00000
## x.Max.
## 1 3.00000
## 2 208.00000
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] ""
## [1] "quantity is significantly influenced by at least one culture for CFM with p= 0.0416451593124405"
## [1] ""
## [1] "Pairwise comparison: "
## Latin American
## Other 0.04469366
## NULL
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable quantity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000 0.00000 0.00000 151.70667 4.50000
## 2 CFM 0.00000 0.00000 0.00000 29.84848 0.00000
## 3 CRM 0.00000 0.00000 0.00000 51.51765 0.00000
## x.Max.
## 1 10847.00000
## 2 362.00000
## 3 1677.00000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE creations_share"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable creations_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.2708333 0.6252637 0.5946545 1.0000000 1.0000000
## 2 CFM 0.0000000 0.2662539 0.3809524 0.4614714 0.7209302 1.0000000
## 3 CRM 0.1538462 0.8947368 0.9731544 0.9053623 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "creations_share is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.183916263 NA
## CRM 0.005294576 9.424154e-07
## [1] "Cohends d for effect size of the CRM on creations_share :"
##
## Cohen's d
##
## d estimate: -1.206611 (large)
## 95 percent confidence interval:
## lower upper
## -1.7130086 -0.7002138
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on creations_share :"
##
## Cohen's d
##
## d estimate: -2.001366 (large)
## 95 percent confidence interval:
## lower upper
## -2.571120 -1.431611
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable creations_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.2465004 0.6007812 0.5902017 0.8957787 1.0000000
## 2 CFM 0.1428571 0.3750000 0.5832896 0.5290579 0.6359890 0.9200000
## 3 CRM 0.5600000 0.7718988 0.9130435 0.8602845 0.9730264 1.0000000
## [1] ""
## [1] ""
## [1] "creations_share is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.53672735 NA
## CRM 0.01464143 7.300159e-05
## [1] "Cohends d for effect size of the CRM on creations_share :"
##
## Cohen's d
##
## d estimate: -0.9515564 (large)
## 95 percent confidence interval:
## lower upper
## -1.4881154 -0.4149973
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on creations_share :"
##
## Cohen's d
##
## d estimate: -1.938248 (large)
## 95 percent confidence interval:
## lower upper
## -2.732731 -1.143764
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable creations_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.4000000 0.5913724 0.5610299 0.8333333 1.0000000
## 2 CFM 0.0000000 0.0000000 0.3333333 0.4056777 0.7142857 0.9807692
## 3 CRM 0.4000000 0.8791209 0.9583333 0.8744286 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "creations_share is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.36237664 NA
## CRM 0.00168505 0.03904341
## [1] "Cohends d for effect size of the CRM on creations_share :"
##
## Cohen's d
##
## d estimate: -1.118202 (large)
## 95 percent confidence interval:
## lower upper
## -1.7185207 -0.5178841
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on creations_share :"
##
## Cohen's d
##
## d estimate: -1.908229 (large)
## 95 percent confidence interval:
## lower upper
## -3.0714421 -0.7450163
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable creations_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.2500000 0.5000000 0.5336449 0.9230769 1.0000000
## 2 CFM 0.2500000 0.5333333 0.9382716 0.7293129 0.9502762 0.9746835
## 3 CRM 0.0000000 0.7204301 0.7857143 0.7650795 0.9250000 1.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE tag_changes_share"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable tag_changes_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.000000000 0.000000000 0.107044868 0.304370109 0.500000000
## 2 CFM 0.000000000 0.002457002 0.267441860 0.293917820 0.486486486
## 3 CRM 0.000000000 0.000000000 0.000000000 0.018927889 0.005802708
## x.Max.
## 1 1.000000000
## 2 1.000000000
## 3 0.418604651
## [1] ""
## [1] ""
## [1] "tag_changes_share is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.5653263389 NA
## CRM 0.0003040143 1.28492e-06
## [1] "Cohends d for effect size of the CRM on tag_changes_share :"
##
## Cohen's d
##
## d estimate: 1.263038 (large)
## 95 percent confidence interval:
## lower upper
## 0.7533697 1.7727062
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on tag_changes_share :"
##
## Cohen's d
##
## d estimate: 1.562976 (large)
## 95 percent confidence interval:
## lower upper
## 1.027359 2.098594
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable tag_changes_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.000000000 0.000000000 0.171750663 0.295323781 0.500000000
## 2 CFM 0.000000000 0.026345178 0.179842715 0.256703767 0.450892857
## 3 CRM 0.000000000 0.000000000 0.005483208 0.048957437 0.097169811
## x.Max.
## 1 1.000000000
## 2 0.857142857
## 3 0.229787234
## [1] ""
## [1] ""
## [1] "tag_changes_share is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.92162630 NA
## CRM 0.01065215 0.01065215
## [1] "Cohends d for effect size of the CRM on tag_changes_share :"
##
## Cohen's d
##
## d estimate: 0.9498737 (large)
## 95 percent confidence interval:
## lower upper
## 0.4134092 1.4863381
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on tag_changes_share :"
##
## Cohen's d
##
## d estimate: 1.256921 (large)
## 95 percent confidence interval:
## lower upper
## 0.5340187 1.9798230
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable tag_changes_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.000000000 0.066666667 0.142857143 0.316311355 0.484848485
## 2 CFM 0.000000000 0.095238095 0.142857143 0.380952381 0.666666667
## 3 CRM 0.000000000 0.000000000 0.000000000 0.037592050 0.004385965
## x.Max.
## 1 1.000000000
## 2 1.000000000
## 3 0.500000000
## [1] ""
## [1] ""
## [1] "tag_changes_share is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 8.114304e-01 NA
## CRM 4.340506e-05 0.01560836
## [1] "Cohends d for effect size of the CRM on tag_changes_share :"
##
## Cohen's d
##
## d estimate: 0.98351 (large)
## 95 percent confidence interval:
## lower upper
## 0.3920478 1.5749723
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on tag_changes_share :"
##
## Cohen's d
##
## d estimate: 1.672336 (large)
## 95 percent confidence interval:
## lower upper
## 0.5392536 2.8054174
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable tag_changes_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.000000000 0.022727273 0.235849057 0.290048252 0.500000000
## 2 CFM 0.000000000 0.004219409 0.018518519 0.221214252 0.333333333
## 3 CRM 0.000000000 0.000000000 0.010733453 0.080153250 0.035087719
## x.Max.
## 1 1.000000000
## 2 0.750000000
## 3 0.763157895
## [1] ""
## [1] ""
## [1] "tag_changes_share is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.57151082 NA
## CRM 0.01636417 0.5715108
## [1] "Cohends d for effect size of the CRM on tag_changes_share :"
##
## Cohen's d
##
## d estimate: 0.8134778 (large)
## 95 percent confidence interval:
## lower upper
## 0.2317254 1.3952301
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE geometry_changes_share"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable geometry_changes_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.000000000 0.000000000 0.000000000 0.077093814 0.001734567
## 2 CFM 0.000000000 0.014742015 0.245454545 0.277570292 0.461538462
## 3 CRM 0.000000000 0.000000000 0.026666667 0.080000103 0.093373494
## x.Max.
## 1 0.666666667
## 2 0.727272727
## 3 0.692307692
## [1] ""
## [1] ""
## [1] "geometry_changes_share is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.001679646 NA
## CRM 0.048452234 0.001871586
## [1] "Cohends d for effect size of the CFM on geometry_changes_share :"
##
## Cohen's d
##
## d estimate: -0.9351151 (large)
## 95 percent confidence interval:
## lower upper
## -1.5279721 -0.3422581
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 2 1 12
## high income
## 10
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean
## 1 low income 0.00000000 0.10000000 0.20000000 0.20000000
## 2 lower middle income 0.39568345 0.39568345 0.39568345 0.39568345
## 3 upper middle income 0.00000000 0.23170996 0.42194570 0.38138762
## 4 high income 0.00000000 0.00000000 0.04047406 0.15669224
## x.3rd Qu. x.Max.
## 1 0.30000000 0.40000000
## 2 0.39568345 0.39568345
## 3 0.57142857 0.66666667
## 4 0.19251337 0.72727273
## [1] ""
## [1] "N:"
## Latin American Other
## 15 10
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean
## 1 Latin American 0.000000000 0.040474061 0.351351351 0.309523836
## 2 Other 0.000000000 0.003685504 0.188948307 0.229639976
## x.3rd Qu. x.Max.
## 1 0.535714286 0.666666667
## 2 0.391963828 0.727272727
## [1] ""
## [1] "Cohends d for effect size of the CRM on geometry_changes_share :"
##
## Cohen's d
##
## d estimate: -0.01932379 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.4902017 0.4515541
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on geometry_changes_share :"
##
## Cohen's d
##
## d estimate: 1.120944 (large)
## 95 percent confidence interval:
## lower upper
## 0.6127743 1.6291147
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable geometry_changes_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.12916112 0.11634846
## 2 CFM 0.02000000 0.11607143 0.31712394 0.27907777 0.39615385
## 3 CRM 0.00000000 0.01959330 0.05263158 0.09921119 0.15194229
## x.Max.
## 1 0.83333333
## 2 0.58518519
## 3 0.33333333
## [1] ""
## [1] ""
## [1] "geometry_changes_share is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.001247722 NA
## CRM 0.035260750 0.001247722
## [1] "Cohends d for effect size of the CFM on geometry_changes_share :"
##
## Cohen's d
##
## d estimate: -0.6792594 (medium)
## 95 percent confidence interval:
## lower upper
## -1.32710918 -0.03140969
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 0 4 5
## high income
## 5
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean
## 1 lower middle income 0.29139073 0.35203550 0.37843290 0.36551241
## 2 upper middle income 0.02000000 0.07142857 0.09375000 0.13417857
## 3 high income 0.10714286 0.18181818 0.40000000 0.35482924
## x.3rd Qu. x.Max.
## 1 0.39190981 0.41379310
## 2 0.14285714 0.34285714
## 3 0.50000000 0.58518519
## [1] ""
## [1] "N:"
## Other
## 14
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 Other 0.0200000 0.1160714 0.3171239 0.2790778 0.3961538 0.5851852
## [1] ""
## [1] "Cohends d for effect size of the CRM on geometry_changes_share :"
##
## Cohen's d
##
## d estimate: 0.1563367 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.3535038 0.6661772
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on geometry_changes_share :"
##
## Cohen's d
##
## d estimate: 1.339173 (large)
## 95 percent confidence interval:
## lower upper
## 0.6089395 2.0694073
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable geometry_changes_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.12160377 0.27417641
## 2 CFM 0.00000000 0.01923077 0.28571429 0.36575092 0.52380952
## 3 CRM 0.00000000 0.00000000 0.02366864 0.06867730 0.07692308
## x.Max.
## 1 0.70312500
## 2 1.00000000
## 3 0.47058824
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable geometry_changes_share"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.20909931 0.41666667
## 2 CFM 0.00000000 0.02109705 0.03591160 0.05708074 0.06172840
## 3 CRM 0.00000000 0.02857143 0.09183673 0.12321189 0.22222222
## x.Max.
## 1 1.00000000
## 2 0.16666667
## 3 0.38461538
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE edit_diversity"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable edit_diversity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.0000000 0.6365142 0.7222595 1.0778798 2.5942634
## 2 CFM 0.0000000 0.6931472 1.5524150 1.5137655 2.1639557 3.1265855
## 3 CRM 0.0000000 0.0000000 0.5004024 0.5169892 0.7896993 2.4583113
## [1] ""
## [1] ""
## [1] "edit_diversity is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.002835903 NA
## CRM 0.480375719 1.474478e-05
## [1] "Cohends d for effect size of the CFM on edit_diversity :"
##
## Cohen's d
##
## d estimate: -0.9523542 (large)
## 95 percent confidence interval:
## lower upper
## -1.5462966 -0.3584117
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 2 1 12
## high income
## 10
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 low income 0.8979457 1.2144482 1.5309507 1.5309507 1.8474532
## 2 lower middle income 2.8971441 2.8971441 2.8971441 2.8971441 2.8971441
## 3 upper middle income 0.1934235 0.9972460 1.7760883 1.5890775 2.1032776
## 4 high income 0.0000000 0.6234114 1.2651640 1.2816163 1.7600927
## x.Max.
## 1 2.1639557
## 2 2.8971441
## 3 2.8009994
## 4 3.1265855
## [1] ""
## [1] "N:"
## Latin American Other
## 15 10
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 Latin American 0.6931472 1.1213230 1.5524150 1.5924010 2.0495837
## 2 Other 0.0000000 0.2951092 1.2886082 1.3958123 2.3407128
## x.Max.
## 1 2.8009994
## 2 3.1265855
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on edit_diversity :"
##
## Cohen's d
##
## d estimate: 1.521997 (large)
## 95 percent confidence interval:
## lower upper
## 0.9892365 2.0547567
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable edit_diversity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.0000000 0.6931472 0.9280879 1.3457079 2.9791941
## 2 CFM 0.6931472 1.0819299 1.4933304 1.5449732 1.5997524 3.0829232
## 3 CRM 0.0000000 0.4970873 0.7667767 0.8811845 1.2552325 2.2242289
## [1] ""
## [1] ""
## [1] "edit_diversity is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.01211749 NA
## CRM 0.82350010 0.01211749
## [1] "Cohends d for effect size of the CFM on edit_diversity :"
##
## Cohen's d
##
## d estimate: -0.7619201 (medium)
## 95 percent confidence interval:
## lower upper
## -1.4134761 -0.1103641
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 0 4 5
## high income
## 5
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 lower middle income 0.8587409 1.0021193 1.2426049 1.2112325 1.4517181
## 2 upper middle income 0.7963116 1.1779832 1.4856815 1.3134275 1.5364650
## 3 high income 0.6931472 1.6094379 2.3419943 2.0435115 2.4900548
## x.Max.
## 1 1.5009793
## 2 1.5706960
## 3 3.0829232
## [1] ""
## [1] "N:"
## Other
## 14
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 Other 0.6931472 1.0819299 1.4933304 1.5449732 1.5997524 3.0829232
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on edit_diversity :"
##
## Cohen's d
##
## d estimate: 1.044023 (large)
## 95 percent confidence interval:
## lower upper
## 0.3382281 1.7498177
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable edit_diversity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.0000000 1.0397208 1.0363653 1.6434263 2.3826934
## 2 CFM 0.0000000 0.1453885 1.0986123 1.0644432 1.7478681 2.3303471
## 3 CRM 0.0000000 0.2062169 0.4724380 0.5535091 0.7589368 1.6094379
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable edit_diversity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.6931472 1.0397208 1.0637037 1.6094379 2.3693821
## 2 CFM 0.3235917 0.3984082 0.4067292 0.8168242 0.9954909 1.9599009
## 3 CRM 0.0000000 0.5211689 1.0320802 1.0142600 1.4109406 2.2640138
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE edit_complexity"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable edit_complexity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 1.000000 1.000000 1.000000 1.576923 2.000000 5.000000
## 2 CFM 1.000000 1.000000 2.000000 1.760000 2.000000 3.000000
## 3 CRM 1.000000 2.000000 2.000000 2.192982 2.000000 3.000000
## [1] ""
## [1] ""
## [1] "edit_complexity is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 7.076192e-02 NA
## CRM 9.401965e-06 0.0006617683
## [1] "Cohends d for effect size of the CRM on edit_complexity :"
##
## Cohen's d
##
## d estimate: -0.9619298 (large)
## 95 percent confidence interval:
## lower upper
## -1.4556750 -0.4681847
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on edit_complexity :"
##
## Cohen's d
##
## d estimate: -0.9274212 (large)
## 95 percent confidence interval:
## lower upper
## -1.4260837 -0.4287586
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable edit_complexity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 1.000000 1.000000 2.000000 1.722222 2.000000 3.000000
## 2 CFM 1.000000 2.000000 2.000000 2.000000 2.000000 3.000000
## 3 CRM 2.000000 2.000000 2.000000 2.148148 2.000000 3.000000
## [1] ""
## [1] ""
## [1] "edit_complexity is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.23265585 NA
## CRM 0.01365483 0.348844
## [1] "Cohends d for effect size of the CRM on edit_complexity :"
##
## Cohen's d
##
## d estimate: -0.7324083 (medium)
## 95 percent confidence interval:
## lower upper
## -1.2579410 -0.2068757
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable edit_complexity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 1.000000 1.000000 2.000000 1.606061 2.000000 4.000000
## 2 CFM 1.000000 2.000000 2.000000 2.200000 3.000000 3.000000
## 3 CRM 2.000000 2.000000 2.000000 2.047619 2.000000 3.000000
## [1] ""
## [1] ""
## [1] "edit_complexity is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.142950477 NA
## CRM 0.003201612 0.4064956
## [1] "Cohends d for effect size of the CRM on edit_complexity :"
##
## Cohen's d
##
## d estimate: -0.7761269 (medium)
## 95 percent confidence interval:
## lower upper
## -1.3559735 -0.1962802
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable edit_complexity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 1.000000 1.000000 1.000000 1.575758 2.000000 3.000000
## 2 CFM 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000
## 3 CRM 1.000000 2.000000 2.000000 2.000000 2.000000 3.000000
## [1] ""
## [1] ""
## [1] "edit_complexity is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.14940678 NA
## CRM 0.02681752 1
## [1] "Cohends d for effect size of the CRM on edit_complexity :"
##
## Cohen's d
##
## d estimate: -0.6830362 (medium)
## 95 percent confidence interval:
## lower upper
## -1.2584993 -0.1075732
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE quality"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable quality"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG -0.01020408 0.00000000 0.03571429 0.28269528 0.58695339
## 2 CFM -0.04761905 0.00000000 0.00000000 0.02976652 0.02727273
## 3 CRM -0.04000000 0.02112676 0.25000000 0.35377339 0.57142857
## x.Max.
## 1 1.00000000
## 2 0.22727273
## 3 1.16666667
## [1] ""
## [1] ""
## [1] "quality is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.08310653 NA
## CRM 0.14946921 1.442479e-05
## [1] "Cohends d for effect size between CRM and CFM on quality :"
##
## Cohen's d
##
## d estimate: -1.108065 (large)
## 95 percent confidence interval:
## lower upper
## -1.6155527 -0.6005775
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable quality"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG -0.33727034 0.00000000 0.02147766 0.13142589 0.20555556
## 2 CFM -0.42857143 0.00000000 0.06586586 0.06880971 0.10379464
## 3 CRM -0.31489362 0.01348678 0.11111111 0.23631937 0.48458677
## x.Max.
## 1 0.70717131
## 2 0.52000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable quality"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG -0.13333333 0.00000000 0.13602550 0.26277187 0.30434783
## 2 CFM 0.00000000 0.01587302 0.02884615 0.16608669 0.28571429
## 3 CRM -0.11764706 0.04761905 0.28947368 0.33971046 0.63414634
## x.Max.
## 1 1.50000000
## 2 0.50000000
## 3 1.06896552
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable quality"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG -1.00000000 0.00000000 0.03636364 0.20716708 0.40000000
## 2 CFM 0.21875000 0.36666667 0.90607735 0.67341992 0.91358025
## 3 CRM 0.00000000 0.08219178 0.44444444 0.40565092 0.64117647
## x.Max.
## 1 2.00000000
## 2 0.96202532
## 3 1.12500000
## [1] ""
## [1] ""
## [1] "quality is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.03564029 NA
## CRM 0.03564029 0.1342881
## [1] "Cohends d for effect size of the CFM on quality :"
##
## Cohen's d
##
## d estimate: -0.9698925 (large)
## 95 percent confidence interval:
## lower upper
## -1.96898473 0.02919966
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 0 0 3
## high income
## 2
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 upper middle income 0.2187500 0.5624137 0.9060773 0.6956176 0.9340513
## 2 high income 0.3666667 0.5033951 0.6401235 0.6401235 0.7768519
## x.Max.
## 1 0.9620253
## 2 0.9135802
## [1] ""
## [1] "N:"
## Other
## 5
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 Other 0.2187500 0.3666667 0.9060773 0.6734199 0.9135802 0.9620253
## [1] ""
## [1] "Cohends d for effect size of the CRM on quality :"
##
## Cohen's d
##
## d estimate: -0.4488552 (small)
## 95 percent confidence interval:
## lower upper
## -1.0156665 0.1179561
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE element_density"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable element_density"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.000000000 0.001583661 0.028424760 0.123349977 0.144928829
## 2 CFM 0.000000000 0.000000000 0.007579649 0.056060249 0.024424192
## 3 CRM 0.000000000 0.004821217 0.013195166 0.165682608 0.090116001
## x.Max.
## 1 0.686575563
## 2 0.436351247
## 3 1.591428030
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable element_density"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.0000000000 0.0007672222 0.0176057247 0.2094394577 0.1532382921
## 2 CFM 0.0033924164 0.0440480009 0.1080512362 0.5880473506 0.6031819993
## 3 CRM 0.0000000000 0.0000280076 0.0009802658 0.1190995113 0.0199959197
## x.Max.
## 1 2.4018896879
## 2 2.1815631003
## 3 1.1588027348
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable element_density"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.0000000000 0.0071655290 0.0455573286 0.1102166689 0.1138113441
## 2 CFM 0.0012594434 0.0183221637 0.1017061091 0.3171414107 0.7126757977
## 3 CRM 0.0000000000 0.0008371309 0.0481353713 0.4711269581 0.3174534647
## x.Max.
## 1 0.7515843768
## 2 0.7517435396
## 3 3.1144483034
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE tag_density"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable tag_density"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.000000 1.371429 2.540794 2.277550 3.261425 5.009355
## 2 CFM 0.000000 0.000000 1.407382 1.448019 2.637216 4.916667
## 3 CRM 0.000000 1.049143 1.159443 1.482301 1.904762 5.831715
## [1] ""
## [1] ""
## [1] "tag_density is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.13564758 NA
## CRM 0.02394484 0.9669319
## [1] "Cohends d for effect size of the CRM on tag_density :"
##
## Cohen's d
##
## d estimate: 0.5832377 (medium)
## 95 percent confidence interval:
## lower upper
## 0.06366428 1.10281113
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"
## NULL
## NULL
##
## Call:
## lm(formula = variable2 ~ classes2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7591 -0.2865 -0.1843 0.1029 3.0726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.0682 2.5236 2.801 0.00969 **
## classes2 -1.4364 0.6468 -2.221 0.03566 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.056 on 25 degrees of freedom
## Multiple R-squared: 0.1648, Adjusted R-squared: 0.1314
## F-statistic: 4.932 on 1 and 25 DF, p-value: 0.03566
##
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable tag_density"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.000000 1.480040 2.131967 2.328615 3.173203 6.436430
## 2 CFM 1.955952 2.291667 2.643478 2.582108 2.785111 3.234332
## 3 CRM 0.000000 1.166667 1.609756 1.570207 2.000000 3.333333
## [1] ""
## [1] ""
## [1] "tag_density is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.63320899 NA
## CRM 0.08875075 0.03999103
## [1] "Cohends d for effect size between CRM and CFM on tag_density :"
##
## Cohen's d
##
## d estimate: 1.367256 (large)
## 95 percent confidence interval:
## lower upper
## 0.2682058 2.4663066
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable tag_density"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.000000 1.784946 2.222222 2.341982 3.087719 4.960573
## 2 CFM 1.090200 1.309353 1.612167 1.717186 1.862312 2.711896
## 3 CRM 0.000000 1.000000 1.051366 1.118664 1.285307 2.826444
## [1] ""
## [1] ""
## [1] "tag_density is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.1596325086 NA
## CRM 0.0007992705 0.05578094
## [1] "Cohends d for effect size of the CRM on tag_density :"
##
## Cohen's d
##
## d estimate: 1.023137 (large)
## 95 percent confidence interval:
## lower upper
## 0.429174 1.617101
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE user_density"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable user_density"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.000000 1.239081 2.158742 1.893394 2.747223 3.500000
## 2 CFM 0.000000 0.000000 1.619197 1.336162 2.358938 3.176471
## 3 CRM 0.000000 1.327098 1.600619 2.198787 2.236111 12.384615
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable user_density"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.000000 1.250000 1.899071 1.767267 2.558030 3.713235
## 2 CFM 1.339985 1.401042 1.901864 1.825766 2.060870 2.425068
## 3 CRM 0.000000 1.692308 2.500287 3.721175 3.804878 20.000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable user_density"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.000000 1.688528 2.161677 1.891375 2.504854 4.128655
## 2 CFM 1.140312 1.931559 2.048327 2.046552 2.112563 3.000000
## 3 CRM 0.000000 1.728142 2.006776 2.423095 2.360269 13.000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE area_diversity"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable area_diversity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.6567309 1.1802398 1.0234693 1.5276886 2.0453477
## 2 CFM 0.0000000 0.0000000 0.5554145 0.5814001 0.8160916 2.0937048
## 3 CRM 0.0000000 0.4588719 0.6450062 0.7192085 1.0820974 1.6933732
## [1] ""
## [1] ""
## [1] "area_diversity is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.04710703 NA
## CRM 0.04710703 0.3255645
## [1] "Cohends d for effect size of the CFM on area_diversity :"
##
## Cohen's d
##
## d estimate: 0.6687833 (medium)
## 95 percent confidence interval:
## lower upper
## 0.02137435 1.31619229
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 0 4 5
## high income
## 5
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 lower middle income 0.0000000 0.0000000 0.0000000 0.1437170 0.1437170
## 2 upper middle income 0.0000000 0.6726303 0.8395389 0.8378637 1.3249397
## 3 high income 0.0000000 0.0000000 0.5359610 0.6750831 0.7457497
## x.Max.
## 1 0.5748679
## 2 1.3522094
## 3 2.0937048
## [1] ""
## [1] "N:"
## Other
## 14
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 Other 0.0000000 0.0000000 0.5554145 0.5814001 0.8160916 2.0937048
## [1] ""
## [1] "Cohends d for effect size of the CRM on area_diversity :"
##
## Cohen's d
##
## d estimate: 0.5146849 (medium)
## 95 percent confidence interval:
## lower upper
## -0.002584912 1.031954706
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable area_diversity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.7697337 1.4388325 1.1185299 1.6396943 1.8865400
## 2 CFM 0.6128310 0.7188128 1.1627699 1.1438574 1.4515277 1.7733455
## 3 CRM 0.0000000 0.0000000 0.7286722 0.7510325 1.1358786 1.6890652
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable area_diversity"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.9441687 1.3917973 1.1781853 1.6785351 1.9820004
## 2 CFM 0.2530907 0.8364142 1.0535875 0.9272660 1.1975143 1.2957235
## 3 CRM 0.0000000 0.2416494 0.6737367 0.6747114 0.9002561 1.8861502
## [1] ""
## [1] ""
## [1] "area_diversity is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.21483191 NA
## CRM 0.01103815 0.2148319
## [1] "Cohends d for effect size of the CRM on area_diversity :"
##
## Cohen's d
##
## d estimate: 0.8242096 (large)
## 95 percent confidence interval:
## lower upper
## 0.2418943 1.4065248
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE economic_distance"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable economic_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.11888112 0.00000000
## 2 CFM 0.00000000 0.00000000 0.00000000 0.04198675 0.00000000
## 3 CRM 0.00000000 1.00000000 1.00000000 0.92424405 1.00000000
## x.Max.
## 1 1.00000000
## 2 1.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "economic_distance is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 3.995521e-01 NA
## CRM 5.842675e-13 2.866003e-14
## [1] "Cohends d for effect size of the CRM on economic_distance :"
##
## Cohen's d
##
## d estimate: -2.86635 (large)
## 95 percent confidence interval:
## lower upper
## -3.512613 -2.220087
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"
## NULL
## NULL
##
## Call:
## lm(formula = variable2 ~ classes2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.97823 0.02177 0.02177 0.02177 0.26296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01346 0.18682 0.072 0.943
## classes2 0.24119 0.04888 4.934 7.83e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2175 on 55 degrees of freedom
## Multiple R-squared: 0.3068, Adjusted R-squared: 0.2942
## F-statistic: 24.35 on 1 and 55 DF, p-value: 7.827e-06
##
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on economic_distance :"
##
## Cohen's d
##
## d estimate: -3.635228 (large)
## 95 percent confidence interval:
## lower upper
## -4.374831 -2.895624
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable economic_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.11605620 0.00000000
## 2 CFM 0.00000000 0.00000000 0.00000000 0.01068616 0.00000000
## 3 CRM 0.00000000 1.00000000 1.00000000 0.96290817 1.00000000
## x.Max.
## 1 1.00000000
## 2 0.14960630
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "economic_distance is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 3.611125e-01 NA
## CRM 3.512493e-11 9.439673e-09
## [1] "Cohends d for effect size of the CRM on economic_distance :"
##
## Cohen's d
##
## d estimate: -3.284846 (large)
## 95 percent confidence interval:
## lower upper
## -4.060462 -2.509231
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on economic_distance :"
##
## Cohen's d
##
## d estimate: -5.995877 (large)
## 95 percent confidence interval:
## lower upper
## -7.491693 -4.500060
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable economic_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.0000000 0.0000000 0.1017330 0.0000000 1.0000000
## 2 CFM 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 3 CRM 0.0000000 1.0000000 1.0000000 0.9042432 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "economic_distance is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 2.775890e-01 NA
## CRM 6.665277e-08 0.0003935208
## [1] "Cohends d for effect size of the CRM on economic_distance :"
##
## Cohen's d
##
## d estimate: -2.85522 (large)
## 95 percent confidence interval:
## lower upper
## -3.641165 -2.069274
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on economic_distance :"
##
## Cohen's d
##
## d estimate: -3.294956 (large)
## 95 percent confidence interval:
## lower upper
## -4.689276 -1.900637
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable economic_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.06632823 0.00000000
## 2 CFM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 3 CRM 0.00000000 1.00000000 1.00000000 0.85815160 1.00000000
## x.Max.
## 1 1.00000000
## 2 0.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "economic_distance is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 3.239594e-01 NA
## CRM 4.379284e-09 0.0003171088
## [1] "Cohends d for effect size of the CRM on economic_distance :"
##
## Cohen's d
##
## d estimate: -3.158135 (large)
## 95 percent confidence interval:
## lower upper
## -3.986158 -2.330111
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on economic_distance :"
##
## Cohen's d
##
## d estimate: -2.957872 (large)
## 95 percent confidence interval:
## lower upper
## -4.288838 -1.626907
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE cultural_distance"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable cultural_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.08041958 0.00000000
## 2 CFM 0.00000000 0.00000000 0.00000000 0.04198675 0.00000000
## 3 CRM 0.00000000 1.00000000 1.00000000 0.94178790 1.00000000
## x.Max.
## 1 1.00000000
## 2 1.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "cultural_distance is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 6.611596e-01 NA
## CRM 1.173131e-14 4.730205e-15
## [1] "Cohends d for effect size of the CRM on cultural_distance :"
##
## Cohen's d
##
## d estimate: -3.565341 (large)
## 95 percent confidence interval:
## lower upper
## -4.289821 -2.840861
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"
## NULL
## NULL
##
## Call:
## lm(formula = variable2 ~ classes2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7458 0.0017 0.0017 0.0017 0.2542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01169 0.14766 -0.079 0.937
## classes2 0.25250 0.03864 6.535 2.17e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1719 on 55 degrees of freedom
## Multiple R-squared: 0.4371, Adjusted R-squared: 0.4269
## F-statistic: 42.71 on 1 and 55 DF, p-value: 2.171e-08
##
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on cultural_distance :"
##
## Cohen's d
##
## d estimate: -4.103755 (large)
## 95 percent confidence interval:
## lower upper
## -4.900356 -3.307153
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable cultural_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.10461899 0.00000000
## 2 CFM 0.00000000 0.00000000 0.00000000 0.01068616 0.00000000
## 3 CRM 0.00000000 1.00000000 1.00000000 0.96290817 1.00000000
## x.Max.
## 1 1.00000000
## 2 0.14960630
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "cultural_distance is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 4.669727e-01 NA
## CRM 2.333447e-11 9.439673e-09
## [1] "Cohends d for effect size of the CRM on cultural_distance :"
##
## Cohen's d
##
## d estimate: -3.525514 (large)
## 95 percent confidence interval:
## lower upper
## -4.333965 -2.717063
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on cultural_distance :"
##
## Cohen's d
##
## d estimate: -5.995877 (large)
## 95 percent confidence interval:
## lower upper
## -7.491693 -4.500060
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable cultural_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.09887073 0.00000000
## 2 CFM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 3 CRM 0.00000000 1.00000000 1.00000000 0.90424316 1.00000000
## x.Max.
## 1 1.00000000
## 2 0.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "cultural_distance is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 2.374395e-01 NA
## CRM 9.847510e-08 0.0003935208
## [1] "Cohends d for effect size of the CRM on cultural_distance :"
##
## Cohen's d
##
## d estimate: -2.966147 (large)
## 95 percent confidence interval:
## lower upper
## -3.767263 -2.165032
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on cultural_distance :"
##
## Cohen's d
##
## d estimate: -3.294956 (large)
## 95 percent confidence interval:
## lower upper
## -4.689276 -1.900637
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable cultural_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.02594502 0.00000000
## 2 CFM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 3 CRM 0.00000000 1.00000000 1.00000000 0.87929282 1.00000000
## x.Max.
## 1 0.34782609
## 2 0.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "cultural_distance is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 3.778002e-01 NA
## CRM 7.276532e-10 0.0003171088
## [1] "Cohends d for effect size of the CRM on cultural_distance :"
##
## Cohen's d
##
## d estimate: -4.216276 (large)
## 95 percent confidence interval:
## lower upper
## -5.204483 -3.228068
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on cultural_distance :"
##
## Cohen's d
##
## d estimate: -3.096082 (large)
## 95 percent confidence interval:
## lower upper
## -4.452552 -1.739613
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE population_density_distance"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable population_density_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.29423766 0.53729839
## 2 CFM 0.00000000 0.00000000 0.00000000 0.08248284 0.00000000
## 3 CRM 0.00000000 1.00000000 1.00000000 0.86210803 1.00000000
## x.Max.
## 1 1.00000000
## 2 1.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "population_density_distance is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 1.857187e-02 NA
## CRM 1.689396e-07 6.918854e-11
## [1] "Cohends d for effect size of the CFM on population_density_distance :"
##
## Cohen's d
##
## d estimate: 0.5927155 (medium)
## 95 percent confidence interval:
## lower upper
## 0.01759147 1.16783955
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 2 1 12
## high income
## 10
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean
## 1 low income 0.00000000 0.00000000 0.00000000 0.00000000
## 2 lower middle income 0.00000000 0.00000000 0.00000000 0.00000000
## 3 upper middle income 0.00000000 0.00000000 0.00000000 0.08409091
## 4 high income 0.00000000 0.00000000 0.00000000 0.10529801
## x.3rd Qu. x.Max.
## 1 0.00000000 0.00000000
## 2 0.00000000 0.00000000
## 3 0.00000000 1.00000000
## 4 0.00000000 1.00000000
## [1] ""
## [1] "N:"
## Latin American Other
## 15 10
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean
## 1 Latin American 0.000000000 0.000000000 0.000000000 0.133939394
## 2 Other 0.000000000 0.000000000 0.000000000 0.005298013
## x.3rd Qu. x.Max.
## 1 0.000000000 1.000000000
## 2 0.000000000 0.052980132
## [1] ""
## [1] "Cohends d for effect size of the CRM on population_density_distance :"
##
## Cohen's d
##
## d estimate: -1.624276 (large)
## 95 percent confidence interval:
## lower upper
## -2.157789 -1.090763
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"
## NULL
## NULL
##
## Call:
## lm(formula = variable2 ~ classes2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.60677 0.06426 0.06426 0.06426 0.62282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.38014 0.21156 -1.797 0.0779 .
## classes2 0.32897 0.05536 5.943 1.99e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2463 on 55 degrees of freedom
## Multiple R-squared: 0.391, Adjusted R-squared: 0.38
## F-statistic: 35.32 on 1 and 55 DF, p-value: 1.989e-07
##
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on population_density_distance :"
##
## Cohen's d
##
## d estimate: -2.578756 (large)
## 95 percent confidence interval:
## lower upper
## -3.202038 -1.955474
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable population_density_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.27473688 0.50431034
## 2 CFM 0.00000000 0.00000000 0.00000000 0.09448718 0.11220472
## 3 CRM 0.00000000 1.00000000 1.00000000 0.91504830 1.00000000
## x.Max.
## 1 1.00000000
## 2 0.81250000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "population_density_distance is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 1.657085e-01 NA
## CRM 1.482570e-07 1.48257e-07
## [1] "Cohends d for effect size of the CRM on population_density_distance :"
##
## Cohen's d
##
## d estimate: -1.909822 (large)
## 95 percent confidence interval:
## lower upper
## -2.522120 -1.297523
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on population_density_distance :"
##
## Cohen's d
##
## d estimate: -3.425136 (large)
## 95 percent confidence interval:
## lower upper
## -4.439578 -2.410694
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable population_density_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.0000000 0.0000000 0.3018222 0.7582978 1.0000000
## 2 CFM 0.0000000 0.0000000 0.0000000 0.2000000 0.0000000 1.0000000
## 3 CRM 0.0000000 0.4856213 1.0000000 0.7383535 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "population_density_distance is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.43985090 NA
## CRM 0.00297149 0.05037693
## [1] "Cohends d for effect size of the CRM on population_density_distance :"
##
## Cohen's d
##
## d estimate: -1.040683 (large)
## 95 percent confidence interval:
## lower upper
## -1.6357821 -0.4455847
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable population_density_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.0000000 0.0000000 0.2780406 0.4347826 1.0000000
## 2 CFM 0.0000000 0.0000000 0.0000000 0.1719056 0.4037267 0.4558011
## 3 CRM 0.0000000 0.4415954 0.7142857 0.6494678 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "population_density_distance is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.690625318 NA
## CRM 0.004457559 0.02201713
## [1] "Cohends d for effect size of the CRM on population_density_distance :"
##
## Cohen's d
##
## d estimate: -0.9535705 (large)
## 95 percent confidence interval:
## lower upper
## -1.543202 -0.363939
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on population_density_distance :"
##
## Cohen's d
##
## d estimate: -1.268177 (large)
## 95 percent confidence interval:
## lower upper
## -2.3574530 -0.1789012
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE physical_geography_distance"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable physical_geography_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.0000000 0.0000000 0.2557346 0.3645833 1.0000000
## 2 CFM 0.0000000 0.0000000 0.0000000 0.1272176 0.0000000 1.0000000
## 3 CRM 0.0000000 1.0000000 1.0000000 0.9242440 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "physical_geography_distance is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 3.364231e-01 NA
## CRM 8.554529e-11 7.064269e-13
## [1] "Cohends d for effect size of the CRM on physical_geography_distance :"
##
## Cohen's d
##
## d estimate: -2.109782 (large)
## 95 percent confidence interval:
## lower upper
## -2.682382 -1.537182
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"
## NULL
## NULL
##
## Call:
## lm(formula = variable2 ~ classes2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.97823 0.02177 0.02177 0.02177 0.26296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01346 0.18682 0.072 0.943
## classes2 0.24119 0.04888 4.934 7.83e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2175 on 55 degrees of freedom
## Multiple R-squared: 0.3068, Adjusted R-squared: 0.2942
## F-statistic: 24.35 on 1 and 55 DF, p-value: 7.827e-06
##
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on physical_geography_distance :"
##
## Cohen's d
##
## d estimate: -2.937302 (large)
## 95 percent confidence interval:
## lower upper
## -3.597788 -2.276817
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable physical_geography_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.0000000 0.0000000 0.1464917 0.1551724 1.0000000
## 2 CFM 0.0000000 0.0000000 0.0000000 0.1810848 0.1875000 1.0000000
## 3 CRM 0.3493450 1.0000000 1.0000000 0.9758469 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "physical_geography_distance is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 9.574789e-01 NA
## CRM 3.578816e-11 4.76409e-08
## [1] "Cohends d for effect size of the CRM on physical_geography_distance :"
##
## Cohen's d
##
## d estimate: -3.524348 (large)
## 95 percent confidence interval:
## lower upper
## -4.332637 -2.716058
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on physical_geography_distance :"
##
## Cohen's d
##
## d estimate: -3.62436 (large)
## 95 percent confidence interval:
## lower upper
## -4.672771 -2.575949
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable physical_geography_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.14461681 0.10000000
## 2 CFM 0.00000000 0.00000000 0.00000000 0.01509434 0.00000000
## 3 CRM 0.00000000 1.00000000 1.00000000 0.92825938 1.00000000
## x.Max.
## 1 1.00000000
## 2 0.07547170
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "physical_geography_distance is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 5.176129e-01 NA
## CRM 3.570673e-08 0.0002426442
## [1] "Cohends d for effect size of the CRM on physical_geography_distance :"
##
## Cohen's d
##
## d estimate: -2.857471 (large)
## 95 percent confidence interval:
## lower upper
## -3.643721 -2.071221
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on physical_geography_distance :"
##
## Cohen's d
##
## d estimate: -4.177999 (large)
## 95 percent confidence interval:
## lower upper
## -5.754289 -2.601708
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable physical_geography_distance"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.0000000 0.0000000 0.0000000 0.1524312 0.1666667 1.0000000
## 2 CFM 0.0000000 0.0000000 0.0000000 0.1123467 0.1059322 0.4558011
## 3 CRM 0.0000000 1.0000000 1.0000000 0.8784061 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "physical_geography_distance is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
## CG CFM
## CFM 8.545558e-01 NA
## CRM 3.345239e-07 0.001182783
## [1] "Cohends d for effect size of the CRM on physical_geography_distance :"
##
## Cohen's d
##
## d estimate: -2.395567 (large)
## 95 percent confidence interval:
## lower upper
## -3.122014 -1.669121
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on physical_geography_distance :"
##
## Cohen's d
##
## d estimate: -2.566349 (large)
## 95 percent confidence interval:
## lower upper
## -3.829002 -1.303695
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE comment_size"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 26 25 57
## [1] ""
## [1] "Summary Statistics for variable comment_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.0000000 0.7500000 2.0000000 3.1115832 3.4583333
## 2 CFM 0.1475410 1.1702128 1.6666667 2.6538324 3.3333333
## 3 CRM 0.0000000 0.6153846 3.0000000 3.3741301 5.5000000
## x.Max.
## 1 18.0000000
## 2 10.0000000
## 3 10.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 36 14 27
## [1] ""
## [1] "Summary Statistics for variable comment_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.0000000 0.7443182 1.9275362 2.1938107 3.0000000
## 2 CFM 0.3333333 0.9226190 2.2767857 4.8841270 5.5000000
## 3 CRM 0.2307692 0.5357143 1.1500000 1.6592537 2.0000000
## x.Max.
## 1 7.0000000
## 2 21.0000000
## 3 5.5000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable comment_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.3333333 1.0000000 2.0000000 2.2777637 3.0000000
## 2 CFM 1.0000000 1.1666667 1.5000000 2.4333333 3.0000000
## 3 CRM 0.1250000 0.8461538 1.9275362 3.0044324 4.5000000
## x.Max.
## 1 7.0000000
## 2 5.5000000
## 3 10.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 33 5 21
## [1] ""
## [1] "Summary Statistics for variable comment_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.1666667 0.7142857 1.5714286 2.2260519 3.0000000 8.8000000
## 2 CFM 0.4090909 1.0000000 1.0000000 1.0212121 1.3333333 1.3636364
## 3 CRM 0.1492537 0.4000000 1.0000000 1.3776457 1.6511628 8.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE discussion_size"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable discussion_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.00000000 0.00000000 0.00000000 0.02666667 0.00000000
## 2 CFM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 3 CRM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## x.Max.
## 1 2.00000000
## 2 0.00000000
## 3 0.00000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable discussion_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.0000000 0.0000000 0.0000000 0.6333333 0.0000000
## 2 CFM 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 3 CRM 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## x.Max.
## 1 47.5000000
## 2 0.0000000
## 3 0.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable discussion_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.00 0.00 0.00 0.12 0.00 9.00
## 2 CFM 0.00 0.00 0.00 0.00 0.00 0.00
## 3 CRM 0.00 0.00 0.00 0.00 0.00 0.00
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable discussion_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0 0 0 0 0 0
## 2 CFM 0 0 0 0 0 0
## 3 CRM 0 0 0 0 0 0
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE notes_size"
## [1] "========================================================================"
## [1] ""
## [1] ""
## [1] ""
## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame: one month"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable notes_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.000000 0.000000 0.000000 1.268889 0.000000 46.000000
## 2 CFM 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 3 CRM 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [1] ""
## [1] ""
## [1] "notes_size is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.18182503 NA
## CRM 0.06422603 NaN
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: six months"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable notes_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.000000 0.000000 0.000000 3.522667 0.000000 56.000000
## 2 CFM 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 3 CRM 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [1] ""
## [1] ""
## [1] "notes_size is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.029119631 NA
## CRM 0.001081845 NaN
## [1] "Cohends d for effect size of the CFM on notes_size :"
##
## Cohen's d
##
## d estimate: 0.3658006 (small)
## 95 percent confidence interval:
## lower upper
## -0.05127991 0.78288105
## [1] ""
## [1] "N:"
## low income lower middle income upper middle income
## 2 4 13
## high income
## 14
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 low income 0 0 0 0 0 0
## 2 lower middle income 0 0 0 0 0 0
## 3 upper middle income 0 0 0 0 0 0
## 4 high income 0 0 0 0 0 0
## [1] ""
## [1] "N:"
## Latin American Other
## 20 13
## [1] ""
## [1] "Summary Statistics: "
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 Latin American 0 0 0 0 0 0
## 2 Other 0 0 0 0 0 0
## [1] ""
## [1] "Cohends d for effect size of the CRM on notes_size :"
##
## Cohen's d
##
## d estimate: 0.4466013 (small)
## 95 percent confidence interval:
## lower upper
## 0.1298386 0.7633640
## [1] ""
## [1] "N:"
## [1] 85
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: one year"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable notes_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu.
## 1 CG 0.0000000 0.0000000 0.0000000 2.9941799 0.0000000
## 2 CFM 0.0000000 0.0000000 0.0000000 0.1717172 0.0000000
## 3 CRM 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## x.Max.
## 1 47.0000000
## 2 5.6666667
## 3 0.0000000
## [1] ""
## [1] ""
## [1] "notes_size is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.111465353 NA
## CRM 0.000816572 0.1127313
## [1] "Cohends d for effect size of the CRM on notes_size :"
##
## Cohen's d
##
## d estimate: 0.4647163 (small)
## 95 percent confidence interval:
## lower upper
## 0.1476361 0.7817965
## [1] ""
## [1] "N:"
## [1] 85
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame: two years"
## [1] ""
## [1] "N:"
## CG CFM CRM
## 75 33 85
## [1] ""
## [1] "Summary Statistics for variable notes_size"
## Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 CG 0.000000 0.000000 0.000000 3.007778 0.000000 54.000000
## 2 CFM 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 3 CRM 0.000000 0.000000 0.000000 0.800000 0.000000 30.000000
## [1] ""
## [1] ""
## [1] "notes_size is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
## CG CFM
## CFM 0.1132692 NA
## CRM 0.1132692 0.2812757
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
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